A supplier selection model for hospitals using a combination of artificial neural network and fuzzy VIKOR

TLDR

Hospital supply chain management faces uncertainties, making supplier evaluation and selection challenging, and the quality of medical equipment and consumables directly affects patient recovery and staff performance, warranting periodic quality assessment protocols. The study proposes a supplier selection model for hospitals that combines artificial neural networks with fuzzy VIKOR. The model was developed through a descriptive study conducted in a military hospital across three phases in 2016. Quality emerged as the most influential factor (weight = 0.165), followed by price, timely delivery, packaging and transport quality, supplier background, and payment terms.

Abstract

The supply chain management of medical equipment and consumer goods is full of uncertainties and unexpected issues and one of the major challenges of hospitals is the suppliers' evaluation and selection. study aimed to provide a model for selecting the best supplier in a hospital using a combination of artificial neural network and fuzzy VIKOR. This was a descriptive study conducted in a military hospital in three phases in 2016. The results showed that the most effective factor in supplier selection was 'quality' (weight = 0.165). After that, the highest weights were, respectively, related to 'price', 'timely delivery', 'packaging and transporting quality', 'the supplier's background' and 'payment terms'. According to the results of the present study, the most important factor influencing the supplier selection was 'quality'. Given the direct impact of medical equipment and consumer goods on patients' recovery and nurses and physicians' proper functioning and performance, it can be recommended that the hospital managers and the staff of purchasing unit should develop the quality assessment protocol of equipment and supplies and it should be evaluated periodically by nurses, physicians and patients.